Using Barcodes to Reduce Errors and Increase Productivity

By John McBride, Senior Sales Executive, Adaptive Data Inc.

For all large manufacturing and assembly plants who have been brought to their knees by suppliers who ship the wrong parts (that make it all the way to your plant floor before being discovered), please consider this message… You’ve got an easy, inexpensive, and VERY effective solution to such problems!

I have been working with manufacturing and assembly plants for decades and have heard countless horror stories of the frequency in which they receive the wrong parts… The parts make it out to the manufacturing and assembly floor, causing production lines to shut down when those wrong parts are discovered. We’ve seen some of the worst cases in the paper, where major automotive, aircraft companies, and other manufacturers had to stop operations due to material supply problems. In this “just in time inventory” environment, one of the worst negative potentials is the threat of incorrect shipments from suppliers.

The stakes can be quite high when such events occur. Depending on the availability of replacement parts and, therefore, the amount of time the production line remains at a standstill, the actual hard costs associated with such events can be staggering. Personally, I have heard first hand stories of tens to hundreds of thousands of dollars expended in some of the worst cases associated with receipt of wrong parts. I know some manufacturers who are charging their suppliers hundreds of dollars for each wrong part shipped.

Some manufacturers have lists of the frequency of such errors from their suppliers, many of which average 2-6 such incidents per month. Without a method to correct the problem, some suppliers have had to drop otherwise great customers due to the charges incurred from wrong shipments. What if there were a method available that would eliminate… Yes, that “eliminate” future wrong shipments? What if that solution cost as little as a couple thousand dollars? Think of the value to suppliers and manufacturers alike!

The solution includes the use of a handheld scanning terminal with a bar code comparison program. The user will log in to use the handheld terminal. The pick list, (i.e.; packing slip, printed purchase order, etc.) will include the proper bar code for each item being picked. All items will have the corresponding bar code printed on them. The user will scan the bar code on the pick list, then scan the bar code on each part (or package) being shipped. If the second bar code scanned matches the first bar code from the pick list, the terminal will accept the selection, time stamp the transaction, and the user will include that scanned part into the shipment. If, however, the second bar code does not match the bar code scanned from the pick list, and error statement will appear on the screen. The error will require resolution before the scanner will operate again.

When starting the process, the user may select whether they wish to save the collected data, or not. In its simplest form of use, the user would select not to save the data. If chosen, each transaction will be erased after it is completed, therefore leaving no record. For those who have been experiencing high charges from manufacturers for mislabeled or incorrect part shipments, they may wish to “Save” the data for those shipments in batch mode or wireless to store the data on a personal computer (PC) in a .csv file. Such data may be viewed in MS Excel, SQL, MS Access, or various other spreadsheet or database applications. The time stamped records in such cases would be stored by the supplier as proof of compliance.

Saves the following fields:

First Scan

Second Scan

Not-Matched flag - 'X' if scans are not the same

Alias (device unique ID)

Date/timestamp

EXAMPLE:

This is a well proven solution. Proprietary studies involving several users (suppliers) of this method have shown Zero errors for all but one unit by the end of the study. The one unit that got through the system was by a supplier who chose not to “Save” the data, therefore the employee preparing the shipment could override the ERROR without consequences. Even in that instance, that supplier went from an average of 50 errors per year (4-5 errors per month) to just that 1 error. The others in the study went from an average of 50 errors per year to ZERO. The supplier went from annual error back charges averaging $20,000 per year, to Zero back charges. Not a bad investment, wouldn’t you say?